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A micromechanical damage model for failure mechanism of corroded stirrups is proposed and a model for damage factor is derived.
In particular, a model for the failure rate (Exponential and Weibull model considered) and an Arrhenius-exponential model for failure rate dependency on the stress level are assumed.
An analytic model for failure shows good agreement with the simulation results and so can be used for qualitative estimates of lifetime.
Numerical results show that our proposed approach is robust across different software projects, and has a better performance with respect to next-step-predictability compared to existing neural network model for failure time prediction.
A model for failure prediction based on an exploitation of Markovov's random processes was designed for ensuring maintenance strategy of equipment for continuous steel casting according to its real technical state.
In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN.
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Panel designs were chosen on the basis of failure mechanism maps, constructed using analytic models for failure initiation.
A novel material degradation model (MDM), which is an essential component of progressive damage models for failure analysis of composite structures, is proposed based on the representative volume element (RVE) method.
Bayesian regression models for failure time and the probability of toxicity as functions of treatment and prognostic covariates are used to define two-dimensional covariate-adjusted treatment effect parameter sets.
The results provide insights into the evolution of adhesion surfaces coupled with promoter molecular slipping out of bulk melts, which are useful for future developments of continuum models for failure of polymeric interfaces.
This work presents a methodology based on mesh morphing techniques for transfer of high-fidelity X-ray computed tomography (CT) data, including manufacturing defects information, into finite element (FE) models for failure prognosis in composite structures.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com